Contextualized personalized insomnia therapy regimen, system, and method

ABSTRACT

A system and method for providing a recommendation of an insomnia therapy leverage various metrics including personal health profile, 24/7 biometrics, behavioral information, and environmental stressors to predict insomnia severity, to build a personalized severity and type insomnia therapy map ranked by historic therapy efficacy, to provide a contextualized personalized therapy recommendation, and to optimize the insomnia therapy.

CROSS-REFERENCE TO PRIOR APPLICATION

This application claims the benefit of U.S. Provisional Application No. 63/054,197, filed on 20 Jul. 2020. This application is hereby incorporated by reference herein.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention pertains to a system and method for providing a recommendation of an insomnia therapy and, in particular, to an apparatus and method to leverage various metrics including personal health profile, 24/7 biometrics, behavioral information, and environmental stressors to predict insomnia severity, to build a personalized severity and type insomnia therapy map ranked by historic therapy efficacy, to provide a contextualized personalized therapy recommendation, and to optimize the insomnia therapy.

2. Description of the Related Art

Insomnia interventions include Cognitive Behavioral Therapy for Insomnia (CBTI) and pharmacologic therapy. CBTI is typically regarded as the gold standard for insomnia as it is typically considered curative. However, CBTI often requires in-person therapy sessions by a trained clinician. Recently, digital CBTI programs have been developed in order to increase the availability of this curative therapy.

However, while CBTI is effective, it typically requires the user to go through a series of modules, including sleep consolidation therapy (or sleep restriction therapy), stimulus control instructions, sleep hygiene education, relaxation techniques, and cognitive techniques. While effective, CBTI requires commitment and behavior change in order for a patient to see successful outcomes, where sleep consolidation therapy, in particular, requires an extended period of time where the user may build a significant amount of sleep debt and may thus have difficulty maintaining daytime alertness. Thus, CBTI, while effective, often suffers from poor adherence, especially with digital versions.

Low adherence to CBTI, especially digital CBTI programs, is an issue that limits effectiveness of insomnia treatment. Specifically, it has been difficult to determine which specific therapy or combination of therapies works for an individual. These difficulties are a significant cause of prolonged unimproved insomnia conditions, associated metabolic conditions, and cognitive deficiencies.

Thus, pharmacotherapy (either OTC (e.g. sedative antihistamines) or prescription) remains the most common intervention for insomnia. However, insomnia sufferers are not given daily dosing strategies, and pharmacotherapy suffers from a risk of tolerance and habituation. Improvements in the treatment of insomnia thus would be desirable.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide an improved system and method for providing insomnia therapy that overcome the shortcomings of conventional systems and methods for providing and assessing insomnia therapy. This object is achieved according to one embodiment of the present invention by providing an apparatus and method that leverage various metrics including one or more of a personal health profile, 24/7 biometrics, behavioral information, and environmental stressors to predict insomnia severity, to build a personalized severity and type insomnia therapy map ranked by historic therapy efficacy, to provide a contextualized personalized therapy recommendation, and to optimize the insomnia therapy

Without knowing what insomnia therapy works for a person and when to apply it on a daily basis, an insomnia therapy cannot achieve its expected efficacy. The system and method of the disclosed and claimed concept advantageously meet these and other objectives. The overall concept of the disclosed and claimed concept includes 24/7 personal and environmental sensing/monitoring, insomnia severity estimation and type trending from insomnia symptoms, insomnia severity prediction from behaviors, biometrics, and stressors (personal and/or environmental), and therapy recommendation from personalized insomnia severity and type therapy mapping, and therapy efficacy evaluation.

The system advantageously includes a predictive model to estimate insomnia severity through monitoring a user's personal profile (e.g. Electronic Health Record (EHR) that documents patient conditions such as chronic conditions, meds, etc.), behaviors, biometrics, and personal/environmental stressors (e.g. temperature, stressful events, stress level, activity level and intensity and timing), sleep metrics (e.g. naps and timing), sleep related behaviors (e.g. caffeine and alcohol consumption and timing) and a recommendation module to provide a personalized insomnia therapy recommendation with the maximum possible efficacy to the user.

Although the core therapy for insomnia is CBTI, it also may be required to additionally prescribe a number of medications if CBTI alone doesn't improve the condition(s). As employed herein, the expression “a number of” and variations thereof shall refer broadly to any non-zero quantity, including a quantity of one. For different types of insomnia (e.g. sleep onset insomnia, sleep maintenance insomnia, etc.), the prescribed medication(s) could be different. For instance, suvorexant and doxepin are recommended for sleep maintenance insomnia, whereas zaleplon, triazolam, and ramelteon are recommended for sleep onset insomnia. Some of the medicines are type-agnostic, such as, eszopiclone, zolpidem, and temazepam. Therefore, the proposed system advantageously considers both insomnia severity and insomnia type when building the personalized insomnia therapy map.

The system also includes an insomnia metrics evaluation module to estimate an insomnia severity and to also trend over time an insomnia type based on various sleep metrics (e.g. Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), number of awakenings and associated durations, Total Sleep Time (TST)) and alertness metrics (e.g. PERCLOS—PERcentage of eyelid CLOSure over the time).

Furthermore, the system includes a therapy efficacy evaluation module to evaluate the efficacy of various insomnia therapies based on a comparison between the actual insomnia severity and the predicted insomnia severity, where the actual insomnia severity is estimated by the aforementioned insomnia metrics evaluation module.

To provide a personalized therapy recommendation, the improved system builds a personalized insomnia therapy map that includes insomnia severity and insomnia type versus therapy and is sorted based on the efficacy of the therapy (i.e. the top of the list has the maximum efficacy). Additionally, the system advantageously provides therapy regimen switching or cycling among available insomnia therapies in order to avoid desensitization to the interventions and potential side effects. Especially when the system recommends a less efficacious therapy, such as to avoid desensitization to a more effective therapy, it may recommend multiple prioritized interventions during a given period of attempted sleep, i.e., for a given evening, in order to maintain efficacy. As the system is continuously used by a user, this insomnia therapy map will be updated to better reflect the various insomnia therapies that have been effective for the person, i.e., the patient, so that the system and insomnia therapy map advantageously support the personalized therapy recommendations.

Accordingly, aspects of the disclosed and claimed concept are provided by an improved method of providing a recommendation of an insomnia therapy to a patient, the general nature of which can be stated as including, for each of a plurality of periods of attempted sleep by the patient, determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model, outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies, determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient, determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity, and updating the insomnia therapy map to reflect the efficacy.

Other aspects of the disclosed and claimed concept are provided by an improved system structured and configured to provide a recommendation of an insomnia therapy to a patient, the general nature of which can be stated as including a processor apparatus that can be generally stated as including a processor and a storage, an input apparatus structured to provide input signals to the processor apparatus and that can be generally stated as including one or more of a sleep metrics sensing module that can be generally stated as including a photoplethysmogram (PPG), an alertness sensing module, an activity metrics sensing module that can be generally stated as including at least one of a step counter and a Global Positioning System (GPS) sensor, a personal and environmental stressor sensing module that can be generally stated as including at least one of a Galvanic Skin Response (GSR) sensor and a room temperature sensor, a related behaviors sensing module, and a personal profile, an output apparatus structured to receive output signals from the processor apparatus and to generate outputs, the storage having stored therein a number of routines which, when executed on the processor, cause the system to perform operations that can be generally stated as including, for each of a plurality of periods of attempted sleep by the patient, determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model, outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies, determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient, determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity, and updating the insomnia therapy map to reflect the efficacy.

These and other objects, features, and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a depiction of an improved system in accordance with an aspect of the disclosed and claimed concept;

FIG. 2 is a depiction of a training phase for building an insomnia severity prediction model of the system of FIG. 1;

FIG. 3 is a depiction of a training phase for building an insomnia therapy map of the system of FIG. 1;

FIG. 4 is a depiction of the system of FIG. 1 in a deployment phase;

FIG. 5 is a further depiction of the system of FIG. 4; and

FIG. 6 is a flow chart depicting certain aspects of an improved method in accordance with the disclosed and claimed concept.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. As used herein, “directly coupled” means that two elements are directly in contact with each other. As used herein, “fixedly coupled” or “fixed” means that two components are coupled so as to move as one while maintaining a constant orientation relative to each other.

As used herein, the word “unitary” means a component is created as a single piece or unit. That is, a component that includes pieces that are created separately and then coupled together as a unit is not a “unitary” component or body. As employed herein, the statement that two or more parts or components “engage” one another shall mean that the parts exert a force against one another either directly or through one or more intermediate parts or components. As employed herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality).

Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.

24/7 Personal and Environmental Sensing/Monitoring:

The disclosed and claimed system 4 and method advantageously collects personal profile data (e.g. EHR (chronic condition(s), medication(s)) and monitors various metrics including but not limited to sleep metrics (e.g. WASO, SOL, SE, TST, number of awakenings, sleep stages), activity metrics (e.g. step count, duration, distance, intensity, timing), alertness metrics (e.g. PERCLOS, nap frequency, nap duration), stressors (e.g. Heart Rate (HR), blood pressure, skin conductance, environment temperature), behaviors (e.g. sleep hygiene, caffeine intake frequency and timing), etc. The proposed system monitors these metrics through multi-modality sensors such as under mattress sensors, wearable sensors, etc. In some embodiment, the disclosed and claimed concept includes subjective responses by a patient to a digital survey (e.g. medication side effects).

Insomnia Severity Estimation and Type Trending from Insomnia Symptoms:

In the disclosed and claimed concept, a patient's insomnia severity is defined by a set of predetermined levels. In the disclosed exemplary embodiment, the predetermined levels are four in quantity (e.g. none, mild, moderate, and severe) based on validated Insomnia Severity Index (ISI). Instead of the determination of an ISI depending on a user's subjective responses to a number of survey questions, the disclosed and claimed concept advantageously uses multiple sensors, such as may include one or more of a photoplethysmogram (PPG), a step counter, a Global Positioning System (GPS) sensor, a HR sensor, a Galvanic Skin Response (GSR) sensor, a room temperature sensor) to obtain a number of objective insomnia-related physiological measures, such as, SOL, WASO, SE, TST, activity timing, duration, and intensity, and alertness, and further develops a formula to integrate all the various metrics into an insomnia severity score (e.g. maximum is 100) and map to a predefined insomnia severity level. The formula can be any type of polynomial or other formula that can combine the various metrics and weight them according to various criteria, etc. In some embodiments, this mapping can be shown as in the following Table 1:

TABLE 1 Mapping between insomnia severity score and insomnia severity level Insomnia Severity Score (0-100) Insomnia Level (4 levels) 0-24  none 25-49  mild 50-74  moderate 75-100 severe

This method can be used for evaluating the actual insomnia severity level while an insomnia therapy (or no therapy) is applied to an individual patient. In addition, this module advantageously trends an insomnia type over a predefined moving time window (e.g. weekly) to inform the therapy recommendation.

Insomnia Severity Prediction from Behaviors, Biometrics, and Stressors (Personal/Environmental):

To build an insomnia severity prediction model 8 of the system 4, the present invention captures the indications/data that are relevant insomnia severity. These indications/data include one or more of sleep metrics (last night), sleep debt, stress level, caffeine intake amount and timing, late and hard exercises, hot or cold room temperature, etc. and are collected through a sleep metrics sensing module 74, a related behaviors sensing module 90, an activity metrics sensing module 78, a personal stressor sensing module 83, an environmental stressor sensing module 84, and a personal profile 92 (e.g. chronic conditions and other conditions). In addition, the system 4 collects insomnia metrics such as sleep metrics (tonight) via a sleep metrics sensing module 72 and alertness metrics (tomorrow) via an alertness sensing module 76 to evaluate the insomnia severity via an insomnia metrics evaluation module 40 that can be said to include the sleep metrics sensing module 72 and the alertness sensing module 76. Based on the collected data sets (insomnia severity, relevant metrics/data), the disclosed and claimed concept advantageously builds a machine learning model in a training phase as illustrated in FIG. 2. In a deployment phase as shown in FIG. 4, this model is used as an insomnia severity prediction model 8 to predict an actual insomnia severity level (e.g. tonight) based on one or more of the relevant behaviors, biometrics, and personal/environmental stressors (e.g. past 24 hours), and projected bedtime, by way of example.

Personalized Severity & Type—Therapy Mapping:

To enable the system 4 to output a personalized insomnia therapy recommendation, the disclosed and claimed concept advantageously creates a personalized insomnia therapy map that tracks insomnia severity and insomnia type based on the history of therapy efficacy in the individual patient.

The disclosed and claimed concept advantageously begins with a default insomnia therapy map that is not yet personalized but that is instead based initially upon population data such as can be derived from clinical guidelines, clinical study results, review articles, etc. As an individual patient uses an insomnia therapy, which is defined herein as being either a single insomnia therapy or multiple therapies combined, the proposed system tracks the specific therapy the actual insomnia severity, and the insomnia type and also evaluates the resultant therapy efficacy so that a personalized insomnia severity and type therapy map can be built based on history of the personalized therapy efficacy.

As CBTI is the core therapy for insomnia and it contains multiple aspects such as, sleep hygiene education, sleep restriction therapy, etc., CBTI therapy contains many possible methods. In addition, CBTI alone might not be enough, and additional pharmacological therapy might be required for an individual. However, some medications are specific to certain type of insomnia. The disclosed and claimed concept thus advantageously also trends the insomnia type of an individual patient over the predefined period of time to inform the proper choice of the medication, if needed. Hence, the disclosed and claimed concept advantageously also includes the insomnia type along with the insomnia severity in the insomnia therapy map.

Furthermore, the disclosed and claimed concept advantageously saves up to a predefined quantity of therapies (up to three, by way of example) for each combination of insomnia severity level and insomnia type so that a plurality of personalized alternative and optional insomnia therapies can be recommended as well. To support the recommendation, the exemplary insomnia therapy map illustrated in Table 2 below is organized as a ranked list for each severity level based on the statistics of efficacy history of an individual.

TABLE 2 Personalized Insomnia Severity and Insomnia Type Therapy Map Insomnia Insomnia type Therapy IDs Severity (Onset--O/ (ID with max Past Efficacies Level Maintenance--M) efficacy-bolded) (Max-bolded) moderate O [3, 17, 25] [80, 68, 74] moderate O & M [4, 11, 39] [55, 77, 69] moderate M [9, 33, 2] [67, 69, 70] severe M [7, 19, 41] [75, 66, 70] severe O & M [31, 8, 53] [65, 68, 60] severe O [6, 22, 40] [60, 50, 58] mild O & M [45, 12, 18] [82, 90, 87] mild M [23, 37, 5] [80, 78, 84] mild O [10, 26,35] [77, 63, 71]

In the disclosed and claimed concept, each therapy ID in Table 2 can be one or more specific therapy methods. For instance, Therapy ID 1 represents CBTI 1 (e.g. “choose a relaxing activity for half an hour before bedtime”) as illustrated in Table 3 below. By way of further example, Therapy ID 7 represents a combination of CBTI 1 and Med N (e.g. eszopiclone). In some embodiments, as an individual patient continuously uses the proposed system, this table becomes enhanced and personalized.

TABLE 3 Relationship of Therapy Identification and Associated Therapy Choices Therapy Med N ID CBTI 1 . . . CBTI K Med 1 . . . (e.g. 15) 1 X 2 X 3 X 4 X X 5 X X X 6 X X 7 X X . . . X M (e.g. 35) X X

Therapy Efficacy Evaluation:

To build a personalized insomnia severity and insomnia type therapy ranked map informed by therapy efficacy, the disclosed and claimed concept advantageously creates a therapy efficacy evaluation module or method to evaluate therapy efficacy. The disclosed and claimed concept advantageously evaluates the therapy efficacy through the following processes.

Sleep quality score: The disclosed and claimed concept advantageously determines a sleep quality score by using various sleep metrics (e.g., using one or more of SE, WASO, number of awakenings, SOL, deep sleep percentage, TST, etc.). The disclosed and claimed concept advantageously monitors the sleep metrics of a patient during the night while a specific therapy is in use. The maximum sleep quality score representing the best sleep quality is defined as 100.

Alertness score: The disclosed and claimed concept advantageously determines a next-day alertness score by using various metrics (e.g. PERCLOS, number of naps, and duration of naps). The disclosed and claimed concept advantageously monitors these metrics during the day after applying a therapy and evaluates the alertness score. The maximum alertness score corresponding to the maximum alertness is defined as 100.

Reduction score of insomnia severity: The disclosed and claimed concept advantageously compares the predicted insomnia severity (i.e. before going to the bed) from the insomnia severity prediction model 8 with the actual insomnia severity informed by the aforementioned sleep quality score and alertness score via an insomnia metrics evaluation module 24 (i.e. measured during the night while a specific therapy is in use) to estimate the reduction score. In some embodiments, the insomnia metrics evaluation module combines the results from the sleep quality score and the alertness score into a composite score (i.e. insomnia severity score) that corresponds to a predefined severity level. In some embodiments, the reduction score is determined via a percentage reduction by using a ratio (e.g. actual insomnia severity score/predicted insomnia severity score). In some embodiments, the reduction score is evaluated by the difference between the predicted insomnia severity score and the actual severity score. The maximum reduction score is defined as 100 which represents the maximum efficacy.

The disclosed and claimed concept advantageously evaluates the therapy efficacy for each therapy used by a patient and saves the therapy efficacy along with the insomnia severity and the insomnia type and the specific therapy in the personalized insomnia therapy map so that the insomnia therapy map reflect the ongoing therapy efficacy in the patient of the various insomnia therapies that have been employed. A building phase for building the insomnia therapy map is illustrated in FIG. 3. This insomnia therapy map evolves as the patient continues to use the system.

Therapy Recommendation:

As in shown in FIG. 3, to create an insomnia therapy map 44, the system 4 advantageously creates a default therapy list (e.g. single therapy or combined therapy) with a number of insomnia severities and a number of insomnia types along with a number of potential therapy efficacies that may be based upon population data or otherwise. The disclosed and claimed system advantageously uses this default list to initially provide an insomnia therapy recommendation. As the patient continues to use the system 4, the personalized insomnia therapy map 44 that lists insomnia severity and insomnia type and that is ranked by corresponding therapy efficacy is built, and the system 4 uses the insomnia therapy map 44 to provides a personalized recommendation to achieve optimized therapy efficacy and to improve sleep quality of the patient as illustrated in FIG. 4.

The system 4 includes a therapy recommendation module 48 that prioritizes therapy swapping in order to avoid desensitization, habituation, or potential side effects from chronic usage of a given therapy regimen. In one example, prescription hypnotics are not recommended for any more than, for instance, seven days per month. In another example, diphenhydramine and doxylamine succinate (OTC sedating antihistamines) quickly build a tolerance effect and are not recommended any more than, for instance, four days per month. In alternative embodiments, the tolerance effect for each sleep intervention is tracked independently and, when a suspected tolerance is detected as having begun to be built, as detected as a reduced efficacy of the therapeutic intervention, as tracked and trended by a therapy efficacy evaluation module 52 of the system, the intervention is not recommended for a specific “abstinence period” (e.g. two weeks).

In some embodiments, “no intervention required” is suggested for nights when predicted insomnia severity is low. Alternatively, typical sleep hygiene advice can be given on nights when predicted insomnia severity is low (e.g. messaging such as, “You've had a great day today and it'll soon be time to be ready for a great night. Remember to take time to wind down before bed tonight. A nice, calming activity before bed helps prepare the mind and body for rest.”). In alternative embodiments, a placebo treatment may be recommended on nights with low to moderate insomnia severity (e.g. lavender, etc.).

The apparatus 4 is depicted in FIGS. 4 and 5. Apparatus 4 can be employed in performing an improved method 100 that is likewise in accordance with the disclosed and claimed concept and at least a portion of which is depicted in a schematic fashion in FIG. 6. Apparatus 4 can be characterized as including a processor apparatus 56 that can be said to include a processor 60 and a storage 64 that are connected with one another. Storage 64 is in the form of a non-transitory storage medium that has stored therein a number of routines 68 that are likewise in the form of a non-transitory storage medium and that include instructions which, when executed on processor 60, cause apparatus 4 to perform certain operations such as are mentioned elsewhere herein.

In addition to the other components of system 4 noted hereinbefore, system 4 includes the sleep metrics sensing module 72 comprising a photoplethysmogram (PPG) 74, the alertness sensing module 76, the activity metrics sensing module 78 comprising at least one of a step counter 80 and a Global Positioning System (GPS) sensor 82, the personal stressor sensing module 83 comprising a Galvanic Skin Response (GSR) sensor 86, the environmental stressor sensing module 84 comprising a room temperature sensor 88, the related behaviors sensing module 90, and the personal profile 92 of the patient. These can all be considered to be a part of an input apparatus 94 of system 4 that provides input signals to processor 60. System 4 further includes an output apparatus 96 that receives output signals from processor 60 and that provides outputs that are detectable by the patient, such as audible outputs, visual outputs, and the like without limitation.

Certain aspects of the improved method 100 noted hereinbefore are depicted in the flow chart shown generally in FIG. 6. For each of a plurality of periods of attempted sleep by the patient, the method 100 performs the operations depicted generally in FIG. 6. For instance, the method 100 includes determining, as at 105, a predicted insomnia severity based upon inputs from the insomnia severity prediction model 8. The method 100 also includes outputting, as at 110, a recommendation of an insomnia therapy based upon the predicted insomnia severity and input from the insomnia therapy map 44. The method 100 also includes determining, as at 115, an actual insomnia severity that is based upon a number of insomnia symptoms in the patient. The various insomnia symptoms can be obtained via input apparatus 94. The method 100 further includes determining, as at 120, an efficacy of the insomnia therapy based upon the predicted insomnia severity and the actual insomnia severity. The method 100 also includes, as at 125, updating the insomnia therapy map 44 to reflect the efficacy. Such operations are repeated, as noted hereinbefore, for each of a plurality of periods of attempted sleep by the patient. Over time, therefore, the insomnia therapy map 44 is gradually personalized to the patient in order to provide improved recommendations of insomnia therapies, which is desirable for the patient. Other benefits will be apparent.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.

Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment. 

What is claimed is:
 1. A method of providing a recommendation of an insomnia therapy to a patient, comprising: for each of a plurality of periods of attempted sleep by the patient: determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model; outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies; determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient; determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity; and updating the insomnia therapy map to reflect the efficacy.
 2. The method of claim 1, further comprising: determining an insomnia type that is based at least in part upon at least one of an insomnia type trend in the patient and the number of insomnia symptoms; and outputting the recommendation of the insomnia therapy further based at least in part upon the insomnia type.
 3. The method of claim 2 wherein the insomnia therapy map comprises a table having a plurality of insomnia severities, a plurality of insomnia types, the plurality of insomnia therapies, and the corresponding efficacies, the insomnia therapy map further comprising, for a given insomnia severity of the plurality of insomnia severities and for a given insomnia type plurality of insomnia types, a plural quantity of insomnia therapies and a plural quantity of corresponding efficacies, and further comprising: for a given period of attempted sleep: determining that the actual insomnia severity is the given insomnia severity; determining that the insomnia type is the given insomnia type; and outputting as the recommendation the insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is the greatest.
 4. The method of claim 3 further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation an insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is other than the greatest.
 5. The method of claim 1, further comprising: detecting as a set of insomnia indication data one or more of: a number of sleep metrics and/or a sleep debt via a sleep metrics sensing module, a number of caffeine intake amounts and timings via a related behaviors sensing module, an exercise intensity and timing via an activity metrics sensing module, a room temperature and/or a stress level via a personal and environmental stressor sensing module, an alertness via an alertness sensing module, and a number of chronic conditions via a personal profile; in a training phase of the insomnia severity prediction model, generating the insomnia severity prediction model by building a machine learning model based at least in part upon at least a portion of the set of insomnia indication data; and deploying the insomnia severity prediction model.
 6. The method of claim 2 further comprising, for a given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of an insomnia therapy from among the plurality of insomnia therapies that includes a pharmacological therapy from among a plurality of pharmacological therapies.
 7. The method of claim 6 further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies other than the pharmacological therapy.
 8. The method of claim 6 further comprising, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies that includes another pharmacological therapy from among a plurality of pharmacological therapies other than the pharmacological therapy.
 9. The method of claim 6, further comprising detecting in the patient a potential tolerance for the pharmacological therapy and, responsive thereto, outputting as the recommendation of the insomnia therapy a recommendation of other than the pharmacological therapy for a predetermined period of time.
 10. The method of claim 1, further comprising detecting as the number of insomnia symptoms at least one of a number of sleep metrics and a number of next day alertness metrics, the number of sleep metrics comprising one or more of a Sleep Efficiency (SE), a Wake After Sleep Onset (WASO), a number of awakenings, a Sleep Onset Latency (SOL), a deep sleep percentage, and a Total Sleep Time (TST), and the number of next day alertness metrics comprising one or more of a PERcentage of eyelid CLOSure over the time (PERCLOS), a number of naps, and a duration of naps.
 11. A system structured and configured to provide a recommendation of an insomnia therapy to a patient, comprising: a processor apparatus comprising a processor and a storage; an input apparatus structured to provide input signals to the processor apparatus and comprising one or more of a sleep metrics sensing module comprising a photoplethysmogram (PPG), an alertness sensing module, an activity metrics sensing module comprising at least one of a step counter and a Global Positioning System (GPS) sensor, a personal and environmental stressor sensing module comprising at least one of a Galvanic Skin Response (GSR) sensor and a room temperature sensor, a related behaviors sensing module, and a personal profile; an output apparatus structured to receive output signals from the processor apparatus and to generate outputs; the storage having stored therein a number of routines which, when executed on the processor, cause the system to perform operations comprising: for each of a plurality of periods of attempted sleep by the patient: determining a predicted insomnia severity that is based at least in part upon an insomnia severity prediction model; outputting a recommendation of an insomnia therapy from among a plurality of insomnia therapies based at least in part upon the predicted insomnia severity and an insomnia therapy map of the patient, the insomnia therapy map including a corresponding efficacy for each of the plurality of insomnia therapies; determining an actual insomnia severity that is based at least in part upon a number of insomnia symptoms in the patient; determining an efficacy of the insomnia therapy based at least in part upon the predicted insomnia severity and the actual insomnia severity; and updating the insomnia therapy map to reflect the efficacy.
 12. The system of claim 11 wherein the operations further comprise: determining an insomnia type that is based at least in part upon at least one of an insomnia type trend in the patient and the number of insomnia symptoms; and outputting the recommendation of the insomnia therapy further based at least in part upon the insomnia type.
 13. The system of claim 12 wherein the insomnia therapy map comprises a table having a plurality of insomnia severities, a plurality of insomnia types, the plurality of insomnia therapies, and the corresponding efficacies, the insomnia therapy map further comprising, for a given insomnia severity of the plurality of insomnia severities and for a given insomnia type plurality of insomnia types, a plural quantity of insomnia therapies and a plural quantity of corresponding efficacies, and wherein the operations further comprise: for a given period of attempted sleep: determining that the actual insomnia severity is the given insomnia severity; determining that the insomnia type is the given insomnia type; and outputting as the recommendation the insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is the greatest.
 14. The system of claim 13 wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation an insomnia therapy from among the plural quantity of insomnia therapies whose corresponding efficacy is other than the greatest.
 15. The system of claim 14 wherein the operations further comprise: detecting as a set of insomnia indication data one or more of: a number of sleep metrics and/or a sleep debt via the sleep metrics sensing module, a number of caffeine intake amounts and timings via the related behaviors sensing module, an exercise intensity and timing via the activity metrics sensing module, a room temperature and/or a stress level via the personal and environmental stressor sensing module, an alertness via the alertness sensing module, and a number of chronic conditions via the personal profile; in a training phase of the insomnia severity prediction model, generating the insomnia severity prediction model by building a machine learning model based at least in part upon at least a portion of the set of insomnia indication data; and deploying the insomnia severity prediction model.
 16. The system of claim 12 wherein the operations further comprise, for a given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of an insomnia therapy from among the plurality of insomnia therapies that includes a pharmacological therapy from among a plurality of pharmacological therapies.
 17. The system of claim 16 wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies other than the pharmacological therapy.
 18. The system of claim 16 wherein the operations further comprise, for another period of attempted sleep subsequent to the given period of attempted sleep, outputting as the recommendation of the insomnia therapy a recommendation of another insomnia therapy from among the plurality of insomnia therapies that includes another pharmacological therapy from among a plurality of pharmacological therapies other than the pharmacological therapy.
 19. The system of claim 16 wherein the operations further comprise detecting in the patient a potential tolerance for the pharmacological therapy and, responsive thereto, outputting as the recommendation of the insomnia therapy a recommendation of other than the pharmacological therapy for a predetermined period of time.
 20. The system of claim 11 wherein the operations further comprise detecting as the number of insomnia symptoms at least one of a number of sleep metrics and a number of next day alertness metrics, the number of sleep metrics comprising one or more of a Sleep Efficiency (SE), a Wake After Sleep Onset (WASO), a number of awakenings, a Sleep Onset Latency (SOL), a deep sleep percentage, and a Total Sleep Time (TST), and the number of next day alertness metrics comprising one or more of a PERcentage of eyelid CLOSure over the time (PERCLOS), a number of naps, and a duration of naps. 